Velocity estimation by using imperfect accelerometer and encoder for rigid contact modeling and control
Bibliographic record
Abstract
Velocity estimation is crucial to certain robotic applications involving high bandwidth modeling and control. In the conventional approaches, the velocities generated from encoders or tachometers are quite noisy and low-pass filters are usually engaged to generate usable velocity signals. The low-pass filter, however, cause non negligible phase lag that may severely affect both modeling and control accuracy in the middle and high frequency range. In this paper, two approaches of using a fusion of an encoder and an imperfect accelerometer are proposed to estimate accurate velocities. The two approaches, namely the two-channel approach and the observer-based approach, estimate velocities by using proper frequency weightings over the encoder and accelerometer signals. The encoder mainly contributes to the low-frequency part of the velocity estimation and the accelerometer mainly contribute to the high-frequency part of the velocity estimation. An adaptive mechanism for estimating the accelerometer gain is also presented. The effectiveness of the two proposed velocity estimation approaches is verified experimentally with respect to a one degree-of-freedom robot in terms of both rigid contact modeling and control
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".